Take-home Exercise 3
1 Introduction
Around 77.9% of dwellings in Singapore are made up of Housing and Development Board (HDB) flats, public housing many residents are familiar with and take pride in.
Though the number of households living in HDB apartments continues to increase, there is a steady fall in number of Singaporeans actually living in these flats. One argument provided is that more Singaporeans are selling their flats and “upgrading” to private housings, like condominium and private landed properties. Among those upgrading, selling their current house to afford for a better HDB apartments is not uncommon too.
On the flip side, there is the demand and there are many reasons for residents to buy resale flats.
- Families may not be keen to wait approximately 2 to 5.9 years for an apartment under the Build-to-Order exercise.
- Couples, being both Permanent Residents, can only access the resale market, if they want to buy a HDB flat.
- Single citizens similarly are only allowed to purchase resale flats.
In spite of the ever increasing resale prices, increasing 8.7% YoY, and much more expensive than a BTO flat, resale flats continue to be in the cards for residents to buy their dream homes.
With how expensive these apartments are, you better bet that people will be choosing where to place their money more carefully.
2 Import Packages
Below the packages that we will be using in this exercise:
3 Import Data
| Dataset | Source |
|---|---|
| HDB Resale Flat Prices | Data.gov.sg |
| Masterplan 2019 Subzone Boundary | Professor Kam Tin Seong |
| Hawker Centres | OpenMapSG |
| Elderly Centres | OpenMapSG |
| Parks | OpenMapSG |
| Kindergartens | OpenMapSG |
| Childcare Centres | OpenMapSG |
| Bus Stops | LTA DataMall |
| MRT Stations | LTA DataMall |
| Primary Schools | OpenMapSG |
| Supermarkets | OpenMapSG |
| Malls | Wikipedia |
| HDB MUP/HIP Status | Housing Development Board |
3.1 Import HDB Resale Flat Prices
# A tibble: 5 × 11
month town flat_…¹ block stree…² store…³ floor…⁴ flat_…⁵ lease…⁶ remai…⁷
<chr> <chr> <chr> <chr> <chr> <chr> <dbl> <chr> <dbl> <chr>
1 2017-01 ANG MO … 2 ROOM 406 ANG MO… 10 TO … 44 Improv… 1979 61 yea…
2 2017-01 ANG MO … 3 ROOM 108 ANG MO… 01 TO … 67 New Ge… 1978 60 yea…
3 2017-01 ANG MO … 3 ROOM 602 ANG MO… 01 TO … 67 New Ge… 1980 62 yea…
4 2017-01 ANG MO … 3 ROOM 465 ANG MO… 04 TO … 68 New Ge… 1980 62 yea…
5 2017-01 ANG MO … 3 ROOM 601 ANG MO… 01 TO … 67 New Ge… 1980 62 yea…
# … with 1 more variable: resale_price <dbl>, and abbreviated variable names
# ¹flat_type, ²street_name, ³storey_range, ⁴floor_area_sqm, ⁵flat_model,
# ⁶lease_commence_date, ⁷remaining_lease3.1.0.1 Convert “Month” to DataTime Format
As we want to have the abilities to filter the data according to its dates, it’s best for us to convert the date column to a DateTime format.
3.1.0.1.1 Data - Filter Jan 2021 to Feb 2023
Here, we will filter the data so we will only be working with those from January 2021 to February 2023.
# A tibble: 5 × 12
month town flat_…¹ block stree…² store…³ floor…⁴ flat_…⁵ lease…⁶ remai…⁷
<chr> <chr> <chr> <chr> <chr> <chr> <dbl> <chr> <dbl> <chr>
1 2021-01 ANG MO … 2 ROOM 170 ANG MO… 01 TO … 45 Improv… 1986 64 yea…
2 2021-01 ANG MO … 2 ROOM 170 ANG MO… 07 TO … 45 Improv… 1986 64 yea…
3 2021-01 ANG MO … 3 ROOM 331 ANG MO… 04 TO … 68 New Ge… 1981 59 yea…
4 2021-01 ANG MO … 3 ROOM 534 ANG MO… 04 TO … 68 New Ge… 1980 58 yea…
5 2021-01 ANG MO … 3 ROOM 561 ANG MO… 01 TO … 68 New Ge… 1980 58 yea…
# … with 2 more variables: resale_price <dbl>, date <date>, and abbreviated
# variable names ¹flat_type, ²street_name, ³storey_range, ⁴floor_area_sqm,
# ⁵flat_model, ⁶lease_commence_date, ⁷remaining_lease3.2 Import Subzone Data
subzone_sf <- st_read(dsn="data/geospatial/MPSZ-2019", layer="MPSZ-2019") %>% st_transform(crs = 3414)Reading layer `MPSZ-2019' from data source
`C:\deadline2359\IS415-GAA\Take-home_Ex\Take-home_Ex03\data\geospatial\MPSZ-2019'
using driver `ESRI Shapefile'
Simple feature collection with 332 features and 6 fields
Geometry type: MULTIPOLYGON
Dimension: XY
Bounding box: xmin: 103.6057 ymin: 1.158699 xmax: 104.0885 ymax: 1.470775
Geodetic CRS: WGS 84Simple feature collection with 6 features and 6 fields
Geometry type: MULTIPOLYGON
Dimension: XY
Bounding box: xmin: 8012.578 ymin: 22108.68 xmax: 35287.9 ymax: 31092.38
Projected CRS: SVY21 / Singapore TM
SUBZONE_N SUBZONE_C PLN_AREA_N PLN_AREA_C REGION_N
1 MARINA EAST MESZ01 MARINA EAST ME CENTRAL REGION
2 INSTITUTION HILL RVSZ05 RIVER VALLEY RV CENTRAL REGION
3 ROBERTSON QUAY SRSZ01 SINGAPORE RIVER SR CENTRAL REGION
4 JURONG ISLAND AND BUKOM WISZ01 WESTERN ISLANDS WI WEST REGION
5 FORT CANNING MUSZ02 MUSEUM MU CENTRAL REGION
6 MARINA EAST (MP) MPSZ05 MARINE PARADE MP CENTRAL REGION
REGION_C geometry
1 CR MULTIPOLYGON (((33222.98 29...
2 CR MULTIPOLYGON (((28481.45 30...
3 CR MULTIPOLYGON (((28087.34 30...
4 WR MULTIPOLYGON (((14557.7 304...
5 CR MULTIPOLYGON (((29542.53 31...
6 CR MULTIPOLYGON (((35279.55 30...3.3 Import Independent Variables
With any creation of a predictive model, comes the independent variables that the predictions will rely on.
3.3.1 Geospatial Datasets
Since this project is pertain to Singapore, OpenMapSG will be helpful for us to get these geospatial datasets with ease.
As you can see below, we will be using a package called onemapsgapi that wraps OpenMapSG API.
Using it is relatively simple:
- One will need to register an account with OpenMapSG
- Get a token using
get_token() - Search for datasets they want in
search_themes() - Optionally, look up on the status on desired dataset through
get_theme_status() - And finally get your data from
get_theme()!
Note
I will have already performed the above steps to acquire my datasets. In our case, I will directly read the geospatial data I have downloaded using st_read().
hawkercentre_sf <- st_read(dsn="data/geospatial/hawkercentre", layer="hawkercentre") %>% st_transform(crs = 3414)Reading layer `hawkercentre' from data source
`C:\deadline2359\IS415-GAA\Take-home_Ex\Take-home_Ex03\data\geospatial\hawkercentre'
using driver `ESRI Shapefile'
Simple feature collection with 125 features and 18 fields
Geometry type: POINT
Dimension: XY
Bounding box: xmin: 103.6974 ymin: 1.272716 xmax: 103.9882 ymax: 1.449017
Geodetic CRS: WGS 84eldercare_sf <- st_read(dsn="data/geospatial/eldercare", layer="eldercare") %>% st_transform(crs = 3414)Reading layer `eldercare' from data source
`C:\deadline2359\IS415-GAA\Take-home_Ex\Take-home_Ex03\data\geospatial\eldercare'
using driver `ESRI Shapefile'
Simple feature collection with 133 features and 4 fields
Geometry type: POINT
Dimension: XY
Bounding box: xmin: 103.7119 ymin: 1.271472 xmax: 103.9561 ymax: 1.439561
Geodetic CRS: WGS 84parks_sf <- st_read(dsn="data/geospatial/nationalparks", layer="nationalparks") %>% st_transform(crs = 3414)Reading layer `nationalparks' from data source
`C:\deadline2359\IS415-GAA\Take-home_Ex\Take-home_Ex03\data\geospatial\nationalparks'
using driver `ESRI Shapefile'
Simple feature collection with 421 features and 2 fields
Geometry type: POINT
Dimension: XY
Bounding box: xmin: 103.6929 ymin: 1.214491 xmax: 104.0538 ymax: 1.462094
Geodetic CRS: WGS 84kindergartens_sf <- st_read(dsn="data/geospatial/kindergartens", layer="kindergartens") %>% st_transform(crs = 3414)Reading layer `kindergartens' from data source
`C:\deadline2359\IS415-GAA\Take-home_Ex\Take-home_Ex03\data\geospatial\kindergartens'
using driver `ESRI Shapefile'
Simple feature collection with 448 features and 5 fields
Geometry type: POINT
Dimension: XY
Bounding box: xmin: 103.6887 ymin: 1.247759 xmax: 103.9717 ymax: 1.455452
Geodetic CRS: WGS 84childcare_sf <- st_read(dsn="data/geospatial/childcare", layer="childcare") %>% st_transform(crs = 3414)Reading layer `childcare' from data source
`C:\deadline2359\IS415-GAA\Take-home_Ex\Take-home_Ex03\data\geospatial\childcare'
using driver `ESRI Shapefile'
Simple feature collection with 1925 features and 5 fields
Geometry type: POINT
Dimension: XY
Bounding box: xmin: 103.6878 ymin: 1.247759 xmax: 103.9897 ymax: 1.462134
Geodetic CRS: WGS 84busstop_sf <- st_read(dsn="data/geospatial/BusStop_Feb2023", layer="BusStop") %>% st_transform(crs = 3414)Reading layer `BusStop' from data source
`C:\deadline2359\IS415-GAA\Take-home_Ex\Take-home_Ex03\data\geospatial\BusStop_Feb2023'
using driver `ESRI Shapefile'
Simple feature collection with 5159 features and 3 fields
Geometry type: POINT
Dimension: XY
Bounding box: xmin: 3970.122 ymin: 26482.1 xmax: 48284.56 ymax: 52983.82
Projected CRS: SVY21mrt_sf <- st_read(dsn="data/geospatial/TrainStation_Feb2023", layer="RapidTransitSystemStation") %>% st_transform(crs = 3414)Reading layer `RapidTransitSystemStation' from data source
`C:\deadline2359\IS415-GAA\Take-home_Ex\Take-home_Ex03\data\geospatial\TrainStation_Feb2023'
using driver `ESRI Shapefile'
Simple feature collection with 220 features and 4 fields
Geometry type: POLYGON
Dimension: XY
Bounding box: xmin: 6068.209 ymin: 27478.44 xmax: 45377.5 ymax: 47913.58
Projected CRS: SVY21supermarkets_sf <- st_read(dsn="data/geospatial/supermarkets", layer="SUPERMARKETS") %>% st_transform(crs = 3414)Reading layer `SUPERMARKETS' from data source
`C:\deadline2359\IS415-GAA\Take-home_Ex\Take-home_Ex03\data\geospatial\supermarkets'
using driver `ESRI Shapefile'
Simple feature collection with 526 features and 8 fields
Geometry type: POINT
Dimension: XY
Bounding box: xmin: 4901.188 ymin: 25529.08 xmax: 46948.22 ymax: 49233.6
Projected CRS: SVY213.3.2 Aspatial Data
4 Data Wrangling
4.1 Central Business District
4.2 Primary Schools
4.2.1 Scraping of Ranking Data
To make full use of primary schools as an independent variable, we need to merge the ranking we get from Schlah’s Primary School Rankings. It provides substantial data in how it derived its ranking, which offers a more objective rank.
We then scraped this data from the website.
rank total schoolInfo.schoolSlug schoolInfo.schoolCode
1 1 186 nanyang-primary-school 5258
2 2 186 tao-nan-school 5240
3 3 186 catholic-high-school 7102
4 4 186 nan-hua-primary-school 5622
5 5 186 st-hilda-s-primary-school 5025
schoolInfo.schoolName schoolInfo.schoolNameZh
1 Nanyang Primary School 南洋小学
2 Tao Nan School
3 Catholic High School 公教中学
4 Nan Hua Primary School 南 华 小 学
5 St. Hilda's Primary School 圣希尔达小学
schoolInfo.schoolLogoUrl
1 https://beta.moe.gov.sg/uploads/nanyang-primary-school.jpg
2 https://beta.moe.gov.sg/uploads/tao-nan-school.jpg
3 https://beta.moe.gov.sg/uploads/catholic-high-school.jpg
4 https://beta.moe.gov.sg/uploads/nan-hua-primary-school.jpg
5 https://beta.moe.gov.sg/uploads/st-hildas-primary-school.jpg
schoolInfo.schoolLevel schoolInfo.schoolZone schoolInfo.schoolType
1 Primary School West Government-Aided School
2 Primary School East Government-Aided School
3 Primary School North Government-Aided School
4 Primary School West Government-Aided School
5 Primary School East Government-Aided School
schoolInfo.schoolNature schoolInfo.schoolSession
1 Co-Ed School Single Session
2 Co-Ed School Single Session
3 Boys' School Single Session
4 Co-Ed School Single Session
5 Co-Ed School Single Session
schoolInfo.website schoolInfo.address schoolInfo.schoolArea
1 https://www.nyps.moe.edu.sg 52 King's Road Bukit Timah
2 https://www.taonan.moe.edu.sg 49 Marine Crescent Marine Parade
3 https://www.catholichigh.moe.edu.sg 9 Bishan Street 22 Bishan
4 https://www.nanhuapri.moe.edu.sg 30 Jalan Lempeng Clementi
5 https://www.shps.moe.edu.sg 2 Tampines Ave 3 Tampines
schoolInfo.postalCode schoolInfo.tel schoolInfo.email
1 268097 64672677 nyps@moe.edu.sg
2 449761 64428307 taonan_sch@moe.edu.sg
3 579767 64589869 chs@moe.edu.sg
4 128806 67788050 nhps@moe.edu.sg
5 529706 67811916 shps@moe.edu.sg
schoolInfo.latitude schoolInfo.longitude
1 1.320847 103.8078
2 1.305285 103.9116
3 1.354723 103.8449
4 1.306116 103.7651
5 1.349350 103.9379
schoolInfo.nearestSlugs
1 raffles-girls-primary-school, singapore-chinese-girls-primary-school, new-town-primary-school, henry-park-primary-school, queenstown-primary-school, methodist-girls-school-primary, anglo-chinese-school-primary, fairfield-methodist-school-primary, alexandra-primary-school, anglo-chinese-school-junior
2 chij-katong-primary, ngee-ann-primary-school, haig-girls-school, st-stephen-s-school, opera-estate-primary-school, eunos-primary-school, kong-hwa-school, maha-bodhi-school, telok-kurau-primary-school, geylang-methodist-school-primary
3 guangyang-primary-school, townsville-primary-school, kuo-chuan-presbyterian-primary-school, teck-ghee-primary-school, ai-tong-school, marymount-convent-school, ang-mo-kio-primary-school, kheng-cheng-school, first-toa-payoh-primary-school, st-gabriel-s-primary-school
4 clementi-primary-school, pei-tong-primary-school, qifa-primary-school, fairfield-methodist-school-primary, henry-park-primary-school, bukit-timah-primary-school, methodist-girls-school-primary, pei-hwa-presbyterian-primary-school, new-town-primary-school, keming-primary-school
5 junyuan-primary-school, tampines-primary-school, poi-ching-school, yumin-primary-school, chongzheng-primary-school, gongshang-primary-school, angsana-primary-school, st-anthony-s-canossian-primary-school, tampines-north-primary-school, red-swastika-school
schoolInfo.schoolStatus
1 Special Assistance Plan (SAP), Gifted Education Programme (GEP)
2 Special Assistance Plan (SAP), Gifted Education Programme (GEP)
3 Special Assistance Plan (SAP), Gifted Education Programme (GEP)
4 Special Assistance Plan (SAP), Gifted Education Programme (GEP)
5 Gifted Education Programme (GEP)
schoolInfo.specialNeeds
1 Allied educators (Learning and behavioural support)
2 Allied educators (Learning and behavioural support)
3 Allied educators (Learning and behavioural support)
4 Allied educators (Learning and behavioural support)
5 Allied educators (Learning and behavioural support)
schoolInfo.schoolMission
1 Developing our pupils to reach their fullest potential within a bicultural environment that is steeped in diligence, prudence, respectability and simplicity, thereby enabling them to contribute to society.
2 To nurture innovative pupils of exemplary character with a love for learning.
3 To establish Catholic High School as a school of distinction in innovative and challenging programmes, a forerunner in character building and a beacon for the mindset of excellence, firmly built upon the foundation of Christian values.
4 Nurturing Gracious Citizens rooted in Values and Chinese Culture
5 As an Anglican school, we are committed to providing a balanced development of body, mind and spirit for our students and nurturing God-fearing citizens for our nation.Our school values are: Serve Humbly, Love Sincerely, Learn Continuously, Lead Wisely, Live ResponsiblyOur School motto: Go Forward
schoolInfo.schoolVision
1 Learners of Character, Leaders in Action
2 Love to Learn and Learn to Love
3 The Catholic High student is a leader, gentleman and bilingual scholar of high integrity and robust character, who is passionate about life, learning and service to others.
4 A School of Engaged Learners who Lead with Character and Serve with a Heart
5 One Hildan Family anchored on Godly values - Nurturing Servant Leaders and Changemakers of Tomorrow
schoolInfo.schoolEchos
1 Nanyang Primary School (NYPS) was established in 1917 as the primary section of The Singapore Nanyang Girls' School. NYPS was separated from the secondary section in 1978 due to increasing pupil enrolment and has functioned as a single-session school since 2004. It was accorded the Special Assistance Plan (SAP) status in 1984. In 1990, it began to offer MOE's Gifted Education Programme (GEP)., The school strives to provide an education which prepares the students beyond academic excellence. With the provision of a bi-cultural learning environment, students are nurtured to be learners of character and leaders who will create a positive impact on their communities and society., The Nanyang Curriculum adopts the Head, Heart and Hands approach for the holistic development of the students, as the school believes that internalisation, reflection and putting into action values and skills, are key to learning. The key leverage for the holistic development of students in the Nanyang Curriculum is differentiation which caters to students' different learning needs. Building on its rich Chinese traditions and values while equipping our students with 21st century skills and competencies underscores curriculum innovation in NYPS., An extensive culture for learning and innovation permeates the school as evident in the whole school embarking on the Professional Learning Community (PLC) journey, introducing Lesson Study (LS) as an additional tool to Action Research (AR) and Learning Circle (LC) for teachers to implement curriculum innovations that will deepen their pedagogical knowledge., The school also believes in establishing strong partnership for the holistic development of students. The key partners comprising the School Management Committee, Nanyang Schools Alumni Association and Parent Teacher Association work collaboratively to preserve the NYPS spirit and identity, reinforcing the values-based culture via role-modelling and their contributions and support.
2 Guided by our mission to nurture innovative pupils of exemplary character with a love for learning, we are committed to nurturing and developing each child to be the best that he or she can be. We are committed to pushing the boundaries of excellence in curriculum innovation to provide an all-round, values-based education for our students. As a Special Assistance Plan (SAP) school, we will continue to provide a holistic Chinese culture learning experience for our pupils. With strong support from our alumni, parents, the Singapore Hokkien Huay Kuan and hard work from our pupils and teachers, the school will be able to help every pupil realise his or her full potential.
3 Founded in 1935 by the late Rev. Fr. Edward Becheras, Catholic High School is one of Singapore's highly regarded institutions under the auspices of the Catholic Archdiocese. As a Special Assistance Plan (SAP) school, CHS is grounded in the philosophy of bilingualism and biculturalism. As a Catholic mission school, love is the motivation behind all our actions. We ensure there is Joy of Learning in school life, and that students develop a Mindset of Excellence. The school's core values underpin every member's attitude, actions and aspirations. Our heritage, alumni and parent support group are the strengths that our school draws upon for our model of education, where every student is a Leader, Gentleman and Bilingual Scholar in the making. Offering the unique dual-track model with both the O-Level and Integrated Programmes (IP), the CHS experience offers a curriculum strong in academic distinction, leadership and character development, and sports and aesthetics excellence. Our IP students will spend their first 4 years in CHS before progressing to Eunoia Junior College to complete the IP.
4 Nan Hua Primary school is a government-aided school which was started in 1917. As, a Special Assistance Plan (SAP) school, our students learn both the English and Chinese as, first languages. We are also one of the nine Gifted Education Programme (GEP) Centres in, Singapore., Guided by the school's vision and mission and with the school's motto (Loyalty, Filial Piety, Humanity, Love, Courtesy, Righteousness, Integrity and Sense of, Shame) as the foundational values, we aim to help every Nan Hua child develop into, gracious citizen rooted in values and Chinese culture who is also a caring contributor of the, society; one who leads with character and serves with a heart., Our success story strongly builds on the basis that it takes 'a village to raise a child'. We, firmly believe is collaborating closely with our key partners such as the members of, our School Management Committee, Alumni Association, Parent Support Group,, various community partners and parents to help our children in their development., Together with our dedicated teaching team and committed administrative and support, staff, we seek to give our best to nurture each child holistically and provide them with a, student-centric, values-driven education., Nan Hua Primary School distinguishes herself to be a school with rich traditions, a culture of excellence and one that prepares our children for the 21st century.
5 St Hilda's Primary School established in 1934 is an Anglican Christian school with boys and girls from multi-ethnic backgrounds Through generations of committed school leaders, staff, church workers and parents, the school has developed a culture of excellence and care. One of the key school strengths is the emphasis on moral values and character development. We have daily devotions and a strong Character and Leadership Development programme. The school challenges our students to do well not only academically but also in their co-curricular activities. Every Hildan through his or her six years of education will grow to be an Articulate, Confident, Innovative and Caring Ambassador.
schoolInfo.motherTongues schoolInfo.affiliatedSchools
1 Chinese nanyang-girls-high-school
2 Chinese
3 Chinese catholic-junior-college, catholic-high-school
4 Chinese
5 Chinese, Malay, Tamil st-hilda-s-secondary-school
admissionRank sportsRank artsRank uniformedGroupsRank
1 11 1 2 3
2 19 7 18 7
3 8 15 5 40
4 1 77 47 124
5 9 39 28 474.2.2 Wrangle Dataset on Primary Schools’ Rankings
You may realise from above that primarySch_ranking that the there are quite a few columns prefixed with “schoolInfo$”, which mean that these columns are actually under the column schoolInfo in primarySch_ranking. Using unnest(), the columns inside schoolInfo will be expanded so that we can more easily access schools’ informations like names, addresses.
# A tibble: 6 × 12
rank school…¹ schoo…² address schoo…³ posta…⁴ schoo…⁵ affil…⁶ admis…⁷ sport…⁸
<int> <chr> <chr> <chr> <chr> <chr> <list> <list> <int> <int>
1 1 Nanyang… West 52 Kin… Bukit … 268097 <chr> <chr> 11 1
2 2 Tao Nan… East 49 Mar… Marine… 449761 <chr> <chr> 19 7
3 3 Catholi… North 9 Bish… Bishan 579767 <chr> <chr> 8 15
4 4 Nan Hua… West 30 Jal… Clemen… 128806 <chr> <chr> 1 77
5 5 St. Hil… East 2 Tamp… Tampin… 529706 <chr> <chr> 9 39
6 6 Henry P… West 1 Holl… Bukit … 278790 <chr> <chr> 12 9
# … with 2 more variables: artsRank <int>, uniformedGroupsRank <int>, and
# abbreviated variable names ¹schoolName, ²schoolZone, ³schoolArea,
# ⁴postalCode, ⁵schoolStatus, ⁶affiliatedSchools, ⁷admissionRank, ⁸sportsRank4.2.3 Merging Geospatial and Aspatial Data of Primary Schools
Using postal codes, we will merge them together!
postalCode rank schoolName schoolZone
1 088256 182 Cantonment Primary School South
2 099757 101 Chij (Kellock) South
3 099840 27 Radin Mas Primary School South
4 109100 168 Blangah Rise Primary School South
5 128104 33 Qifa Primary School West
address schoolArea schoolStatus affiliatedSchools
1 1 Cantonment Close Bukit Merah
2 1 Bukit Teresa Road Bukit Merah chij-st-theresa-s-convent
3 1 Bukit Purmei Avenue Bukit Merah
4 91 Telok Blangah Heights Bukit Merah
5 50 West Coast Avenue Clementi
admissionRank sportsRank artsRank uniformedGroupsRank
1 132 153 163 153
2 104 29 82 163
3 10 74 83 13
4 132 167 173 61
5 61 26 10 106
SCHOOLNAME SCH_HSE_BLK_NUM HSE_BLK_NUM SCH_ROAD_NAME
1 CANTONMENT PRIMARY SCHOOL 1 1 CANTONMENT CLOSE
2 CHIJ (KELLOCK) 1 1 BUKIT TERESA ROAD
3 RADIN MAS PRIMARY SCHOOL 1 1 BUKIT PURMEI AVENUE
4 BLANGAH RISE PRIMARY SCHOOL 91 91 TELOK BLANGAH HEIGHTS
5 QIFA PRIMARY SCHOOL 50 50 WEST COAST AVENUE
geometry
1 POINT (28735.3 28695.24)
2 POINT (27413.63 28588.52)
3 POINT (26951.05 28619.79)
4 POINT (25215.83 28728.96)
5 POINT (19496.7 32798.72)But sadly the merged sf has 180 rows while the original geospatial dataset has 181 rows, meaning there is a school with mismatch data.
4.2.3.0.1 Finding Mismatched Row
We will first order primarySch_ranking_sf according to their ranking.
We then loop through the primarySch_ranking, the original DataFrame, to find the schools not in the merged sf.
Note that there are actually 186 rows in primarySch_ranking, meaning there is a difference of 6 rows.
schools_dont_exist = list()
for (i in 1:nrow(primarySch_ranking)){
if (i %in% primarySch__order_ranking_sf$rank == FALSE){
schools_dont_exist <- append(schools_dont_exist, i)
}
}
schools <- data.frame()
for (sch in schools_dont_exist){
schools = rbind(schools, primarySch_ranking[primarySch_ranking$rank == sch,])
}
schools# A tibble: 6 × 12
rank school…¹ schoo…² address schoo…³ posta…⁴ schoo…⁵ affil…⁶ admis…⁷ sport…⁸
<int> <chr> <chr> <chr> <chr> <chr> <list> <list> <int> <int>
1 116 Pioneer… West 23 Jur… Jurong… 649076 <chr> <chr> 132 103
2 119 Stamfor… South 1 Vict… Central 198423 <chr> <chr> 132 42
3 134 Eunos P… East 95 Jal… Geylang 419529 <chr> <chr> 132 88
4 137 Guangya… South 6 Bish… Bishan 579806 <chr> <chr> 132 145
5 165 Juying … West 31 Jur… Jurong… 649037 <chr> <chr> 132 116
6 171 Angsana… East 3 Tamp… Tampin… 529366 <chr> <chr> 132 150
# … with 2 more variables: artsRank <int>, uniformedGroupsRank <int>, and
# abbreviated variable names ¹schoolName, ²schoolZone, ³schoolArea,
# ⁴postalCode, ⁵schoolStatus, ⁶affiliatedSchools, ⁷admissionRank, ⁸sportsRankSo… we need to look through all the current situations of all 6 schools and determine, which one is the one in OneMapSG’s geospatial data.
- Pioneer Primary School merged in Juying Primary School in 2021. In addition, the merged school is moving to a new location in 2026, hence not opening from its Primary 1 Registration from 2021 to 2024
- Stamford Primary School has merged with Farrer Park Primary School in 2023.
- Eunos Primary School has merged with Telok Kurau Primary School.
- Guangyang Primary School has merged with Townsville Primary School.
With that, Angsana Primary School is the row that has its postal code different.
Checking the school’s website, Angsana Primary School’s postal code is actually 528565. Hence we need to change primarySch_ranking’s postal code to facilitate merging.
4.3 Masterplan Subzone 2019
Looking at the subzone dataset, it seems that there are invalid geometries in it.
Thus, we will be using st_make_valid() to correct them.
4.4 MRT
Similarly for MRT, there are invalid geometries as well.
Simple feature collection with 3 features and 4 fields (with 1 geometry empty)
Geometry type: POLYGON
Dimension: XY
Bounding box: xmin: 26568.45 ymin: 27478.44 xmax: 28080.89 ymax: 37543.25
Projected CRS: SVY21 / Singapore TM
TYP_CD STN_NAM TYP_CD_DES STN_NAM_DE
159 0 <NA> MRT HARBOURFRONT MRT STATION
NA NA <NA> <NA> <NA>
218 0 <NA> MRT UPPER THOMSON MRT STATION
geometry
159 POLYGON ((26736.44 27495.44...
NA POLYGON EMPTY
218 POLYGON ((27808.12 37518.2,...However, some geometries have less than 4 polygons, which st_make_valid() does not resolve. Thus we will be excluding them from our exercise.
4.5 HDB
The sf DataFrame we are acquired from Data.gov.sg does not have any geospatial data. But it has the block and streetname that we can derived the data from, through OpenMapSG API.
library(httr)
geocode <- function(block, streetname="") {
base_url <- "https://developers.onemap.sg/commonapi/search"
address <- paste(block, streetname, sep = " ")
query <- list("searchVal" = address,
"returnGeom" = "Y",
"getAddrDetails" = "N",
"pageNum" = "1")
res <- GET(base_url, query = query)
restext<-content(res, as="text")
output <- fromJSON(restext) %>%
as.data.frame %>%
select(results.LATITUDE, results.LONGITUDE)
return(output)
}resale_prices_total$LATITUDE <- 0
resale_prices_total$LONGITUDE <- 0
for (i in 1:nrow(resale_prices_total)){
temp_output <- geocode(resale_prices_total[i, 4], resale_prices_total[i, 5])
resale_prices_total$LATITUDE[i] <- temp_output$results.LATITUDE
resale_prices_total$LONGITUDE[i] <- temp_output$results.LONGITUDE
}resale_prices_sf <- st_read(dsn="data/geospatial/resale_prices", layer="resale_prices") %>% st_transform(crs = 3414)Reading layer `resale_prices' from data source
`C:\deadline2359\IS415-GAA\Take-home_Ex\Take-home_Ex03\data\geospatial\resale_prices'
using driver `ESRI Shapefile'
Simple feature collection with 60218 features and 12 fields
Geometry type: POINT
Dimension: XY
Bounding box: xmin: 103.6442 ymin: 1.27038 xmax: 103.9878 ymax: 1.457071
Geodetic CRS: WGS 844.5.1 Merge with HDB Status
We also have data on the upgrades done on these buildings. It will be good to separate the both two types of upgrades.
Note that MUP is actually then the precessor of HIP before 2007. Differentiating the upgrades then ables us to see if the timing of upgrades has an impact on the resell prices.
4.5.1.1 Home Improvement Programme (HIP)
HIP is described as HDB’s programme to “resolve common maintenance problems of ageing flats”.
4.5.1.2 Main Upgrading Programme (MUP)
Before 2007, MUP was dedicated to do the same things.
4.6 Malls
Finally, we have the mall’s data from Wikipedia. Similar to the HDB flats, we don’t have geospatial data. But thankfully, we can do the same thing using OneMapSG to get the coordinates.
Reading layer `malls' from data source
`C:\deadline2359\IS415-GAA\Take-home_Ex\Take-home_Ex03\data\geospatial\malls'
using driver `ESRI Shapefile'
Simple feature collection with 167 features and 2 fields
Geometry type: POINT
Dimension: XY
Bounding box: xmin: 103.6784 ymin: 1.263932 xmax: 103.9895 ymax: 1.448194
Geodetic CRS: WGS 844.7 Top Primary Schools
It is difficult to really determine what is the appropriate number of top schools to filter for, since schools can very much have their own specialties and histories (like alumni communities) that can affect the data, outside of the ones used by the website we scrapped the ranking data from.
Hence top 20 is admittedly derived after I looked through the dataset and judged that the top 20 schools are really known to be some of the best primary schools, which sentiments I believe will be similar among the general Singapore population.
5 Proximity
Function to acquire proximity
resale_proximity_sf <- resale_prices_upgrade_sf
resale_proximity_sf <- proximity_calculator(original_sf = resale_proximity_sf, derived_sf = cbd_sf, column_name = "PROX_CBD")
resale_proximity_sf <- proximity_calculator(original_sf = resale_proximity_sf, derived_sf = hawkercentre_sf, column_name="PROX_HAWKER")
resale_proximity_sf <- proximity_calculator(original_sf = resale_proximity_sf, derived_sf = eldercare_sf, column_name = "PROX_ELDERCARE")
proximity_calculator(original_sf = resale_proximity_sf, derived_sf = parks_sf, column_name="PROX_PARK")
resale_proximity_sf <- proximity_calculator(original_sf = resale_proximity_sf, derived_sf = kindergartens_sf, column_name = "PROX_KINDERGARTEN")
resale_proximity_sf <- proximity_calculator(original_sf = resale_proximity_sf, derived_sf = childcare_sf, column_name = "PROX_CHILDCARE")
resale_proximity_sf <- proximity_calculator(original_sf = resale_proximity_sf, derived_sf = busstop_sf, column_name = "PROX_BUSSTOP")
resale_proximity_sf <- proximity_calculator(original_sf = resale_proximity_sf, derived_sf = mrt_sf, column_name = "PROX_MRT")
resale_proximity_sf <- proximity_calculator(original_sf = resale_proximity_sf, derived_sf = primarySch_ranking_sf, column_name = "PROX_SCH")
resale_proximity_sf <- proximity_calculator(original_sf = resale_proximity_sf, derived_sf = supermarkets_sf, column_name="PROX_SUPERMARKET")
resale_proximity_sf <- proximity_calculator(original_sf = resale_proximity_sf, derived_sf = malls_sf, column_name = "PROX_MALL")
resale_proximity_sf <- proximity_calculator(original_sf = resale_proximity_sf, derived_sf = top_primarySch_ranking_sf, column_name = "PROX_TOPSCH")5.1 Within radius
resale_proximity_sf <- radius_calculator(resale_proximity_sf, kindergartens_sf, "NUM_KINDERGARTEN", 350)
resale_proximity_sf <- radius_calculator(resale_proximity_sf, childcare_sf, "NUM_CHILDCARE", 350)
resale_proximity_sf <- radius_calculator(resale_proximity_sf, busstop_sf, "NUM_BUSSTOP", 350)
resale_proximity_sf <- radius_calculator(resale_proximity_sf, primarySch_ranking_sf, "NUM_SCH", 1000)
resale_proximity_sf <- radius_calculator(resale_proximity_sf, top_primarySch_ranking_sf, "NUM_TOPSCH", 1000)Reading layer `resale_proximity' from data source
`C:\deadline2359\IS415-GAA\Take-home_Ex\Take-home_Ex03\data\geospatial\resale_proximity'
using driver `ESRI Shapefile'
Simple feature collection with 60218 features and 32 fields
Geometry type: POINT
Dimension: XY
Bounding box: xmin: 6958.193 ymin: 28097.64 xmax: 45192.3 ymax: 48741.06
Projected CRS: SVY21 / Singapore TM6 Get Proximity Locational Factors
6.1 Floor Level
[1] "07 TO 09" "16 TO 18" "01 TO 03" "13 TO 15" "10 TO 12" "04 TO 06"
[7] "19 TO 21" "22 TO 24" "25 TO 27" "34 TO 36" "31 TO 33" "37 TO 39"
[13] "40 TO 42" "28 TO 30" "43 TO 45" "49 TO 51" "46 TO 48"storey_names <- 1:length(storey_category)
storey_order <- data.frame(storey_category, storey_names)
storey_order storey_category storey_names
1 07 TO 09 1
2 16 TO 18 2
3 01 TO 03 3
4 13 TO 15 4
5 10 TO 12 5
6 04 TO 06 6
7 19 TO 21 7
8 22 TO 24 8
9 25 TO 27 9
10 34 TO 36 10
11 31 TO 33 11
12 37 TO 39 12
13 40 TO 42 13
14 28 TO 30 14
15 43 TO 45 15
16 49 TO 51 16
17 46 TO 48 17resale_level_loc_sf <- merge(resale_proximity_sf, storey_order, by.x = "stry_rn", by.y = "storey_category", all.x=TRUE)
resale_level_loc_sfSimple feature collection with 60218 features and 33 fields
Geometry type: POINT
Dimension: XY
Bounding box: xmin: 6958.193 ymin: 28097.64 xmax: 45192.3 ymax: 48741.06
Projected CRS: SVY21 / Singapore TM
First 10 features:
stry_rn block strt_nm month town flt_typ flr_r_s
1 01 TO 03 117B JLN TENTERAM 2021-08 KALLANG/WHAMPOA 4 ROOM 93
2 01 TO 03 101A LOR 2 TOA PAYOH 2021-10 TOA PAYOH EXECUTIVE 145
3 01 TO 03 57 LOR 5 TOA PAYOH 2021-08 TOA PAYOH 3 ROOM 61
4 01 TO 03 305A ANCHORVALE LINK 2021-06 SENGKANG 5 ROOM 110
5 01 TO 03 237 HOUGANG ST 21 2022-05 HOUGANG EXECUTIVE 148
6 01 TO 03 538 ANG MO KIO AVE 5 2023-02 ANG MO KIO 3 ROOM 68
7 01 TO 03 358 CLEMENTI AVE 2 2021-07 CLEMENTI 4 ROOM 92
8 01 TO 03 461 SEGAR RD 2021-02 BUKIT PANJANG 4 ROOM 93
9 01 TO 03 424D YISHUN AVE 11 2022-11 YISHUN 3 ROOM 67
10 01 TO 03 401 HOUGANG AVE 10 2021-07 HOUGANG 3 ROOM 68
flt_mdl ls_cmm_ rmnng_l rsl_prc date HIP
1 Model A 2017 95 years 03 months 655000 2021-08-01 <NA>
2 Apartment 1993 70 years 07 months 898000 2021-10-01 <NA>
3 Standard 1973 50 years 06 months 260000 2021-08-01 <NA>
4 Improved 2002 79 years 09 months 445000 2021-06-01 <NA>
5 Maisonette 1984 61 years 03 months 760000 2022-05-01 <NA>
6 New Generation 1980 56 years 02 months 358000 2023-02-01 HIP
7 New Generation 1978 55 years 07 months 422000 2021-07-01 HIP
8 Premium Apartment 2015 93 years 07 months 390000 2021-02-01 <NA>
9 Model A 2015 92 years 03 months 360000 2022-11-01 <NA>
10 New Generation 1985 62 years 11 months 325000 2021-07-01 HIP
TOWN_x MUP TOWN_y PROX_CB PROX_HA PROX_EL PROX_KI PROX_CH PROX_BU
1 <NA> <NA> <NA> 0.013 0.122 0.115 0.371 0.799 2.842
2 <NA> <NA> <NA> 0.012 0.114 0.033 0.422 1.905 2.629
3 <NA> MUP TOA PAYOH 0.012 0.115 0.033 0.423 0.902 0.934
4 <NA> <NA> <NA> 0.013 0.008 0.066 0.272 1.102 0.465
5 <NA> <NA> <NA> 0.013 0.079 0.042 0.113 1.366 4.606
6 ANG MO KIO <NA> <NA> 0.013 0.020 0.013 0.002 1.797 0.824
7 CLEMENTI <NA> <NA> 0.003 0.047 0.103 0.007 0.937 1.001
8 <NA> <NA> <NA> 0.012 0.038 0.045 0.255 1.691 0.739
9 <NA> <NA> <NA> 0.013 0.124 0.072 0.049 0.891 2.761
10 HOUGANG <NA> <NA> 0.013 0.008 0.095 0.107 1.587 2.062
PROX_MR PROX_SC PROX_SU PROX_MA PROX_TO NUM_KIN NUM_CHI NUM_BUS NUM_SCH
1 0.075 0.033 0.074 0.057 0.007 0 1 6 3
2 0.037 0.028 0.232 0.019 0.007 2 4 10 5
3 0.037 0.028 0.232 0.019 0.007 1 5 11 3
4 0.129 0.082 0.433 0.114 0.015 0 4 8 4
5 0.156 0.079 0.317 0.116 0.014 1 4 8 2
6 0.056 0.094 0.040 0.098 0.016 1 3 10 1
7 0.044 0.007 0.159 0.161 0.001 1 3 9 2
8 0.077 0.127 0.330 0.131 0.019 2 8 6 4
9 0.086 0.165 0.502 0.106 0.016 0 2 6 3
10 0.008 0.072 0.252 0.111 0.014 3 6 9 5
NUM_TOP storey_names geometry
1 0 3 POINT (31058.62 34487.27)
2 1 3 POINT (29552 35767.3)
3 1 3 POINT (30022.91 35470.15)
4 0 3 POINT (34103.93 41148.01)
5 0 3 POINT (34180.87 37633.89)
6 0 3 POINT (30229 39731.65)
7 0 3 POINT (20851.34 32875.25)
8 0 3 POINT (21095.91 40990.18)
9 0 3 POINT (29769.97 44954.8)
10 1 3 POINT (35086.31 39648.81)6.2 Remaining Lease
str_list <- str_split(resale_level_loc_sf$rmnng_l, " ")
for (i in 1:length(str_list)) {
if (length(unlist(str_list[i])) > 2) {
year <- as.numeric(unlist(str_list[i])[1])
month <- as.numeric(unlist(str_list[i])[3])
resale_level_loc_sf[i, "remaining_lease"] <- (year * 12) + month
}
else {
year <- as.numeric(unlist(str_list[i])[1])
resale_level_loc_sf[i, "remaining_lease"] <- (year * 12)
}
}
resale_remaining_lease_loc_sf <- resale_level_loc_sfresale_remaining_lease_loc_sf <- st_read(dsn="data/geospatial/resale_proximity", layer="resale_proximity") %>% st_transform(crs = 3414)Reading layer `resale_proximity' from data source
`C:\deadline2359\IS415-GAA\Take-home_Ex\Take-home_Ex03\data\geospatial\resale_proximity'
using driver `ESRI Shapefile'
Simple feature collection with 60218 features and 32 fields
Geometry type: POINT
Dimension: XY
Bounding box: xmin: 6958.193 ymin: 28097.64 xmax: 45192.3 ymax: 48741.06
Projected CRS: SVY21 / Singapore TM6.3 Age of Unit
6.4 Upgrades
resale_proximity_sf <- st_read(dsn="data/geospatial/resale_upgrade_loc_sf", layer="resale_upgrade_loc_sf")Reading layer `resale_upgrade_loc_sf' from data source
`C:\deadline2359\IS415-GAA\Take-home_Ex\Take-home_Ex03\data\geospatial\resale_upgrade_loc_sf'
using driver `ESRI Shapefile'
Simple feature collection with 60218 features and 32 fields
Geometry type: POINT
Dimension: XY
Bounding box: xmin: 6958.193 ymin: 28097.64 xmax: 45192.3 ymax: 48741.06
Projected CRS: SVY21 / Singapore TM6.5 HDB Types
[1] "3 ROOM" "5 ROOM" "4 ROOM" "EXECUTIVE"
[5] "2 ROOM" "MULTI-GENERATION" "1 ROOM" resale_type_loc_sf <- resale_upgrade_loc_sf %>% filter(resale_upgrade_loc_sf$flt_typ == "3 ROOM")
resale_type_loc_sfSimple feature collection with 13779 features and 34 fields
Geometry type: POINT
Dimension: XY
Bounding box: xmin: 11597.97 ymin: 28097.64 xmax: 45192.3 ymax: 48622.47
Projected CRS: SVY21 / Singapore TM
First 10 features:
block strt_nm month town flt_typ stry_rn flr_r_s
1 1 BEACH RD 2021-02 KALLANG/WHAMPOA 3 ROOM 07 TO 09 68
2 1 BEACH RD 2021-03 KALLANG/WHAMPOA 3 ROOM 07 TO 09 68
3 1 BEACH RD 2021-07 KALLANG/WHAMPOA 3 ROOM 16 TO 18 68
4 1 BEACH RD 2022-12 KALLANG/WHAMPOA 3 ROOM 07 TO 09 68
5 1 BEACH RD 2022-07 KALLANG/WHAMPOA 3 ROOM 01 TO 03 68
6 1 BEACH RD 2021-04 KALLANG/WHAMPOA 3 ROOM 07 TO 09 68
7 1 BEDOK STH AVE 1 2021-01 BEDOK 3 ROOM 07 TO 09 59
8 1 BEDOK STH AVE 1 2021-04 BEDOK 3 ROOM 01 TO 03 59
9 1 BEDOK STH AVE 1 2021-05 BEDOK 3 ROOM 13 TO 15 59
10 1 BEDOK STH AVE 1 2021-09 BEDOK 3 ROOM 10 TO 12 67
flt_mdl ls_cmm_ rmnng_l rsl_prc date HIP TOWN_x MUP
1 Improved 1979 57 years 08 months 328000 2021-02-01 <NA> <NA> MUP
2 Improved 1979 57 years 07 months 350888 2021-03-01 <NA> <NA> MUP
3 Improved 1979 57 years 03 months 355000 2021-07-01 <NA> <NA> MUP
4 Improved 1979 56 years 418000 2022-12-01 <NA> <NA> MUP
5 Improved 1979 56 years 03 months 350000 2022-07-01 <NA> <NA> MUP
6 Improved 1979 57 years 05 months 328000 2021-04-01 <NA> <NA> MUP
7 Improved 1976 54 years 01 month 268000 2021-01-01 <NA> <NA> <NA>
8 Improved 1976 53 years 10 months 265000 2021-04-01 <NA> <NA> <NA>
9 Improved 1976 53 years 08 months 301000 2021-05-01 <NA> <NA> <NA>
10 Improved 1976 53 years 05 months 322000 2021-09-01 <NA> <NA> <NA>
TOWN_y PROX_CB PROX_HA PROX_EL PROX_KI PROX_CH PROX_BU PROX_MR
1 KALLANG/WHAMPOA 0.011 0.105 0.024 0.297 0.510 4.712 0.001
2 KALLANG/WHAMPOA 0.011 0.105 0.024 0.297 0.510 4.712 0.001
3 KALLANG/WHAMPOA 0.011 0.105 0.024 0.297 0.510 4.712 0.001
4 KALLANG/WHAMPOA 0.011 0.105 0.024 0.297 0.510 4.712 0.001
5 KALLANG/WHAMPOA 0.011 0.105 0.024 0.297 0.510 4.712 0.001
6 KALLANG/WHAMPOA 0.011 0.105 0.024 0.297 0.510 4.712 0.001
7 <NA> 0.013 0.029 0.028 0.324 0.727 1.535 0.040
8 <NA> 0.013 0.029 0.028 0.324 0.727 1.535 0.040
9 <NA> 0.013 0.029 0.028 0.324 0.727 1.535 0.040
10 <NA> 0.013 0.029 0.028 0.324 0.727 1.535 0.040
PROX_SC PROX_SU PROX_MA PROX_TO NUM_KIN NUM_CHI NUM_BUS NUM_SCH NUM_TOP
1 0.017 0.418 0.042 0.006 0 2 15 0 0
2 0.017 0.418 0.042 0.006 0 2 15 0 0
3 0.017 0.418 0.042 0.006 0 2 15 0 0
4 0.017 0.418 0.042 0.006 0 2 15 0 0
5 0.017 0.418 0.042 0.006 0 2 15 0 0
6 0.017 0.418 0.042 0.006 0 2 15 0 0
7 0.047 0.321 0.065 0.011 0 4 5 1 0
8 0.047 0.321 0.065 0.011 0 4 5 1 0
9 0.047 0.321 0.065 0.011 0 4 5 1 0
10 0.047 0.321 0.065 0.011 0 4 5 1 0
geometry year age
1 POINT (30309.27 30830.82) 1979-01-01 42
2 POINT (30309.27 30830.82) 1979-01-01 42
3 POINT (30309.27 30830.82) 1979-01-01 42
4 POINT (30309.27 30830.82) 1979-01-01 44
5 POINT (30309.27 30830.82) 1979-01-01 43
6 POINT (30309.27 30830.82) 1979-01-01 42
7 POINT (39173.81 33678.86) 1976-01-01 45
8 POINT (39173.81 33678.86) 1976-01-01 45
9 POINT (39173.81 33678.86) 1976-01-01 45
10 POINT (39173.81 33678.86) 1976-01-01 466.6 save
final_resale_prices <- resale_type_loc_sf
final_resale_prices <- final_resale_prices %>%
rename("storey_range" = "stry_rn",
"street_name" = "strt_nm",
"flat_type" = "flt_typ",
"floor_area_sqm" = "flr_r_s",
"flat_model" = "flt_mdl",
"lease_commence_date" = "ls_cmm_",
"remaining_lease_original" = "rmnng_l",
"resale_price" = "rsl_prc",
"PROX_CBD" = "PROX_CB",
"PROX_HAWKER" = "PROX_HA",
"PROX_ELDERCARE" = "PROX_EL",
"PROX_KINDERGARTEN" = "PROX_KI",
"PROX_CHILDCARE" = "PROX_CH",
"PROX_BUSSTOP" = "PROX_BU",
"PROX_MRT" = "PROX_MR",
"PROX_SCH" = "PROX_SC",
"PROX_TOPSCH" = "PROX_TO",
"PROX_SUPERMARKET" = "PROX_SU",
"PROX_MALL" = "PROX_MA",
"NUM_KINDERGARTEN" = "NUM_KIN",
"NUM_CHILDCARE" = "NUM_CHI",
"NUM_BUSSTOP" = "NUM_BUS",
"NUM_TOPSCH" = "NUM_TOP")
names(final_resale_prices) [1] "block" "street_name"
[3] "month" "town"
[5] "flat_type" "storey_range"
[7] "floor_area_sqm" "flat_model"
[9] "lease_commence_date" "remaining_lease_original"
[11] "resale_price" "date"
[13] "HIP" "TOWN_x"
[15] "MUP" "TOWN_y"
[17] "PROX_CBD" "PROX_HAWKER"
[19] "PROX_ELDERCARE" "PROX_KINDERGARTEN"
[21] "PROX_CHILDCARE" "PROX_BUSSTOP"
[23] "PROX_MRT" "PROX_SCH"
[25] "PROX_SUPERMARKET" "PROX_MALL"
[27] "PROX_TOPSCH" "NUM_KINDERGARTEN"
[29] "NUM_CHILDCARE" "NUM_BUSSTOP"
[31] "NUM_SCH" "NUM_TOPSCH"
[33] "year" "age"
[35] "geometry" 7 Exploratory Data Analysis
7.1 Statistical Graphics
7.1.1 Central Business District
Simple feature collection with 5 features and 6 fields
Geometry type: MULTIPOLYGON
Dimension: XY
Bounding box: xmin: 28896.26 ymin: 27914.19 xmax: 30794.28 ymax: 29517.49
Projected CRS: SVY21 / Singapore TM
SUBZONE_N SUBZONE_C PLN_AREA_N PLN_AREA_C REGION_N REGION_C
1 ANSON DTSZ10 DOWNTOWN CORE DT CENTRAL REGION CR
2 TANJONG PAGAR DTSZ09 DOWNTOWN CORE DT CENTRAL REGION CR
3 MAXWELL DTSZ07 DOWNTOWN CORE DT CENTRAL REGION CR
4 CECIL DTSZ08 DOWNTOWN CORE DT CENTRAL REGION CR
5 CENTRAL SUBZONE DTSZ11 DOWNTOWN CORE DT CENTRAL REGION CR
geometry
1 MULTIPOLYGON (((29201.07 28...
2 MULTIPOLYGON (((29612.3 286...
3 MULTIPOLYGON (((29341.73 29...
4 MULTIPOLYGON (((29917.52 29...
5 MULTIPOLYGON (((30436.73 29...7.1.2 Hawker Centres
Simple feature collection with 5 features and 18 fields
Geometry type: POINT
Dimension: XY
Bounding box: xmin: 22042.51 ymin: 29650.7 xmax: 35955.52 ymax: 46139.03
Projected CRS: SVY21 / Singapore TM
NAME DESCRIP
1 Market Street Hawker Centre New Replacement Centre
2 Marsiling Mall Hawker Centre New Replacement Centre
3 Margaret Drive Hawker Centre New Replacement Centre
4 Fernvale Hawker Centre & Market New Centre
5 One Punggol Hawker Centre New Centre
ADDRESSBU ADDRESSBL ADDRESSP
1 CapitaSpring 50 48940
2 Marsiling Mall 4 738623
3 <NA> 38A 142038
4 Fernvale Community Club, Hawker Centre & Market 21 797650
5 One Punggol 1 828629
ADDRESSS LANDXAD LANDYAD STATUS AWARDED
1 Market Street 29874.82 29650.7 Existing (replacement) 17/7/2017
2 Woodlands Street 12 22042.51 46139.03 Existing (replacement) 4/9/2015
3 Margaret Drive 24816.7 31094.91 Existing (replacement) 11/5/2015
4 Sengkang West Avenue 32867.9 41500.77 Existing (new) 30/7/2018
5 Punggol Drive 35955.52 43336.13 Existing (new) 8/8/2018
IMPLEME
1 1/8/2017
2 1/1/2016
3 2/12/2015
4 13/8/2018
5 29/8/2018
INFO_ON
1 hawker centre/commercial space
2 hawker centre/supermarket/shops
3 hawker centre/branch office/supermarket/shops/flats
4 hawker centre/market/community club
5 hawker centre/Regional Library/Senior Care Centre/Branch Office/Child Development Centre/Blood Collection Centre/Kidney Dialysis Centre/Childcare Centre
EST_ORI ICON_NA
1 15/11/2021 HC icons_Opt 8.jpg
2 8/11/2017 HC icons_Opt 8.jpg
3 30/3/2022 HC icons_Opt 8.jpg
4 22/3/2022 HC icons_Opt 8.jpg
5 29/4/2022 HC icons_Opt 8.jpg
PHOTOUR
1 <NA>
2 http://www.nea.gov.sg/images/default-source/Hawker-Centres-Division/mmhc.jpg
3 <NA>
4 <NA>
5 <NA>
ADDRESS_ APPROXI HUP_COM
1 <NA> <NA> <NA>
2 Blk 4,Woodlands St 12,Singapore?738623 3661 <NA>
3 <NA> <NA> <NA>
4 <NA> <NA> <NA>
5 <NA> <NA> <NA>
geometry
1 POINT (29874.82 29650.7)
2 POINT (22042.51 46139.03)
3 POINT (24816.7 31094.91)
4 POINT (32867.9 41500.77)
5 POINT (35955.52 43336.13)7.1.3 Eldercare Centres
Simple feature collection with 5 features and 4 fields
Geometry type: POINT
Dimension: XY
Bounding box: xmin: 14481.92 ymin: 30135.49 xmax: 38803.81 ymax: 36639.12
Projected CRS: SVY21 / Singapore TM
NAME ADDRESSP
1 Yuhua Senior Activity Centre 601318
2 THK SAC @ Kaki Bukit 462509
3 THK SAC @ Boon Lay 640190
4 PEACE-Connect Senior Activity Centre@5 190005
5 THK SAC @ Beo Crescent 160044
ADDRESSS ICON_NA
1 318A Jurong East Avenue 1 #02-308 onemap-fc-eldercare.png
2 Blk 509B Bedok North Street 3 #02-157 onemap-fc-eldercare.png
3 Blk 190 Boon Lay Drive #01-242 onemap-fc-eldercare.png
4 5 Beach Rd #02-4943 onemap-fc-eldercare.png
5 Blk 44 Beo Crescent #01-67 onemap-fc-eldercare.png
geometry
1 POINT (16614.08 36639.12)
2 POINT (38803.81 35098.77)
3 POINT (14481.92 36357.61)
4 POINT (31505.35 31853.52)
5 POINT (27218.35 30135.49)7.1.4 Parks
Simple feature collection with 5 features and 4 fields
Geometry type: POINT
Dimension: XY
Bounding box: xmin: 14481.92 ymin: 30135.49 xmax: 38803.81 ymax: 36639.12
Projected CRS: SVY21 / Singapore TM
NAME ADDRESSP
1 Yuhua Senior Activity Centre 601318
2 THK SAC @ Kaki Bukit 462509
3 THK SAC @ Boon Lay 640190
4 PEACE-Connect Senior Activity Centre@5 190005
5 THK SAC @ Beo Crescent 160044
ADDRESSS ICON_NA
1 318A Jurong East Avenue 1 #02-308 onemap-fc-eldercare.png
2 Blk 509B Bedok North Street 3 #02-157 onemap-fc-eldercare.png
3 Blk 190 Boon Lay Drive #01-242 onemap-fc-eldercare.png
4 5 Beach Rd #02-4943 onemap-fc-eldercare.png
5 Blk 44 Beo Crescent #01-67 onemap-fc-eldercare.png
geometry
1 POINT (16614.08 36639.12)
2 POINT (38803.81 35098.77)
3 POINT (14481.92 36357.61)
4 POINT (31505.35 31853.52)
5 POINT (27218.35 30135.49)7.1.5 Kindergartens
Simple feature collection with 5 features and 5 fields
Geometry type: POINT
Dimension: XY
Bounding box: xmin: 27354.73 ymin: 38859.25 xmax: 30705.05 ymax: 46300.26
Projected CRS: SVY21 / Singapore TM
NAME DESCRIP
1 PCF Sparkletots Preschool @ Cheng San-Seletar Blk 435 (KN) Kindergartens
2 PCF Sparkletots Preschool @ Cheng San-Seletar Blk 533 (KN) Kindergartens
3 PCF Sparkletots Preschool @ Cheng San-Seletar Blk 556 (DS) Kindergartens
4 PCF Sparkletots Preschool @ Chong Pang Blk 107 (KN) Kindergartens
5 PCF Sparkletots Preschool @ Chong Pang Blk 122 (KN) Kindergartens
ADDRESSP ADDRESSS ICON_NA
1 560435 435 Ang Mo Kio Avenue 10 #01-1393 S(560435) school.gif
2 560533 533 Ang Mo Kio Avenue 5 #01-4100 S(560533) school.gif
3 560556 556 Ang Mo Kio Avenue 10 #01-1902 S(560556) school.gif
4 760107 107 Yishun Ring Road #01-207 S(760107) school.gif
5 760122 122 Yishun Street 11 #01-479 S(760122) school.gif
geometry
1 POINT (30325.45 38859.25)
2 POINT (30190.51 39574.18)
3 POINT (30705.05 39337.9)
4 POINT (27354.73 45992.92)
5 POINT (27755.87 46300.26)7.1.6 Childcare Centres
Simple feature collection with 5 features and 5 fields
Geometry type: POINT
Dimension: XY
Bounding box: xmin: 17828.84 ymin: 29221.89 xmax: 40985.94 ymax: 45530.47
Projected CRS: SVY21 / Singapore TM
NAME DESCRIP ADDRESSP
1 APOLLO INTERNATIONAL PRESCHOOL PRIVATE LIMITED Child Care Services 467903
2 APPLE TREE PLAYHOUSE PTE LTD Child Care Services 768019
3 Appleland Montessori Child Care Centre Pte Ltd Child Care Services 650165
4 APPLELAND PLAYHOUSE Child Care Services 103104
5 APRICOT ACADEMY (LAGUNA) PTE. LTD. Child Care Services 449290
ADDRESSS
1 44, LIMAU GARDEN, BEDOK PARK, SINGAPORE 467903
2 1, NORTHPOINT DRIVE, #02 - 201, NORTHPOINT CITY, SINGAPORE 768019
3 165, BUKIT BATOK WEST AVENUE 8, #01 - 286, SINGAPORE 650165
4 104C, DEPOT ROAD, #01 - 03, SINGAPORE 103104
5 5000G, MARINE PARADE ROAD, #01 - 28/30, LAGUNA PARK, SINGAPORE 449290
ICON_NA geometry
1 onemap-fc-childcare.png POINT (40985.94 33848.38)
2 onemap-fc-childcare.png POINT (28308.65 45530.47)
3 onemap-fc-childcare.png POINT (17828.84 36607.36)
4 onemap-fc-childcare.png POINT (25579.73 29221.89)
5 onemap-fc-childcare.png POINT (38981.02 32483.41)7.1.7 Bus Stops
Simple feature collection with 5 features and 3 fields
Geometry type: POINT
Dimension: XY
Bounding box: xmin: 13228.59 ymin: 30391.85 xmax: 41603.76 ymax: 44206.38
Projected CRS: SVY21 / Singapore TM
BUS_STOP_N BUS_ROOF_N LOC_DESC geometry
1 22069 B06 OPP CEVA LOGISTICS POINT (13576.31 32883.65)
2 32071 B23 AFT TRACK 13 POINT (13228.59 44206.38)
3 44331 B01 BLK 239 POINT (21045.1 40242.08)
4 96081 B05 GRACE INDEPENDENT CH POINT (41603.76 35413.11)
5 11561 B05 BLK 166 POINT (24568.74 30391.85)7.1.8 MRT Stations
Simple feature collection with 5 features and 4 fields
Geometry type: POLYGON
Dimension: XY
Bounding box: xmin: 29286.74 ymin: 30548.91 xmax: 34623.54 ymax: 33404.47
Projected CRS: SVY21 / Singapore TM
TYP_CD STN_NAM TYP_CD_DES STN_NAM_DE
1 0 <NA> MRT ESPLANADE MRT STATION
2 0 <NA> MRT PAYA LEBAR MRT STATION
3 0 <NA> MRT DHOBY GHAUT MRT STATION
4 0 <NA> MRT DAKOTA MRT STATION
5 0 <NA> MRT LAVENDER MRT STATION
geometry
1 POLYGON ((30566.07 30621.21...
2 POLYGON ((34495.6 33384.44,...
3 POLYGON ((29293.51 31312.53...
4 POLYGON ((34055.08 32290.62...
5 POLYGON ((31236.5 32085.76,...7.1.9 Primary Schools
Simple feature collection with 5 features and 16 fields
Geometry type: POINT
Dimension: XY
Bounding box: xmin: 19496.7 ymin: 28588.52 xmax: 28735.3 ymax: 32798.72
Projected CRS: SVY21 / Singapore TM
postalCode rank schoolName schoolZone
1 088256 182 Cantonment Primary School South
2 099757 101 Chij (Kellock) South
3 099840 27 Radin Mas Primary School South
4 109100 168 Blangah Rise Primary School South
5 128104 33 Qifa Primary School West
address schoolArea schoolStatus affiliatedSchools
1 1 Cantonment Close Bukit Merah
2 1 Bukit Teresa Road Bukit Merah chij-st-theresa-s-convent
3 1 Bukit Purmei Avenue Bukit Merah
4 91 Telok Blangah Heights Bukit Merah
5 50 West Coast Avenue Clementi
admissionRank sportsRank artsRank uniformedGroupsRank
1 132 153 163 153
2 104 29 82 163
3 10 74 83 13
4 132 167 173 61
5 61 26 10 106
SCHOOLNAME SCH_HSE_BLK_NUM HSE_BLK_NUM SCH_ROAD_NAME
1 CANTONMENT PRIMARY SCHOOL 1 1 CANTONMENT CLOSE
2 CHIJ (KELLOCK) 1 1 BUKIT TERESA ROAD
3 RADIN MAS PRIMARY SCHOOL 1 1 BUKIT PURMEI AVENUE
4 BLANGAH RISE PRIMARY SCHOOL 91 91 TELOK BLANGAH HEIGHTS
5 QIFA PRIMARY SCHOOL 50 50 WEST COAST AVENUE
geometry
1 POINT (28735.3 28695.24)
2 POINT (27413.63 28588.52)
3 POINT (26951.05 28619.79)
4 POINT (25215.83 28728.96)
5 POINT (19496.7 32798.72)7.1.10 Supermarkets
Simple feature collection with 5 features and 8 fields
Geometry type: POINT
Dimension: XY
Bounding box: xmin: 32184.01 ymin: 32947.46 xmax: 41320.3 ymax: 42685.17
Projected CRS: SVY21 / Singapore TM
LIC_NAME BLK_HOUSE STR_NAME
1 LI LI CHENG SUPERMARKET (PUNGGOL) PTE. LTD. 273C PUNGGOL PLACE
2 SHENG SIONG SUPERMARKET PTE LTD 11 UPPER BOON KENG ROAD
3 COLD STORAGE SINGAPORE (1983) PTE LTD 683 HOUGANG AVENUE 8
4 COLD STORAGE SINGAPORE (1983) PTE LTD 631 BEDOK RESERVOIR ROAD
5 YES SUPERMARKET PTE LTD 201B TAMPINES STREET 21
UNIT_NO POSTCODE LIC_NO INC_CRC FMEL_UPD_D
1 884 823273 NE12I65N000 3DE8AF6E76F9D3D4 2017-11-29
2 901 380011 E73010V000 F361759A8261CD6E 2017-11-29
3 903 530683 NE11909C000 1DC69902E02077CE 2017-11-29
4 954 470631 S02210X000 4E2560154B58BA38 2017-11-29
5 1091 522201 S02037J000 559A9A00D9FF8A55 2017-11-29
geometry
1 POINT (35561.22 42685.17)
2 POINT (32184.01 32947.46)
3 POINT (33903.48 39480.46)
4 POINT (37083.82 35017.47)
5 POINT (41320.3 37283.82)8 OLS Multiple Linear Regression Model
8.1 Filtering
8.1.1 Train
8.1.2 Test
8.2 Visualising the relationships of the independent variables
corrplot(cor(total_resale_prices), diag = FALSE, order = "AOE",
tl.pos = "td", tl.cex = 0.5, method = "number", type = "upper")
Looks like age of the HDB flats and remaining lease are practically inversely correlated, which makes sense. So one will be removed, age will be removed.
set.seed(1234)
resale_price.mlr <- lm(formula = resale_price ~ floor_area_sqm + HIP + MUP + PROX_CBD + PROX_HAWKER + PROX_ELDERCARE + PROX_KINDERGARTEN + PROX_CHILDCARE + PROX_BUSSTOP + PROX_MRT + PROX_SUPERMARKET + PROX_MALL + PROX_TOPSCH + NUM_KINDERGARTEN + NUM_CHILDCARE + NUM_BUSSTOP + NUM_TOPSCH, data = train_resale_prices)
ols_regress(resale_price.mlr)set.seed(1234)
resale_price.mlr <- lm(formula = resale_price ~ floor_area_sqm + HIP + MUP + PROX_CBD + PROX_HAWKER + PROX_ELDERCARE + PROX_CHILDCARE + PROX_SUPERMARKET + PROX_MALL + PROX_TOPSCH + NUM_KINDERGARTEN + NUM_CHILDCARE + NUM_SCH + NUM_TOPSCH, data = train_resale_prices)
ols_regress(resale_price.mlr)
Call:
lm(formula = resale_price ~ floor_area_sqm + HIP + MUP + PROX_CBD +
PROX_HAWKER + PROX_ELDERCARE + PROX_CHILDCARE + PROX_SUPERMARKET +
PROX_MALL + PROX_TOPSCH + NUM_KINDERGARTEN + NUM_CHILDCARE +
NUM_SCH + NUM_TOPSCH, data = train_resale_prices)
Residuals:
Min 1Q Median 3Q Max
-209212 -43894 -8586 26338 378127
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 217932.0 15906.7 13.701 < 2e-16 ***
floor_area_sqm 4379.0 216.3 20.248 < 2e-16 ***
HIP -49887.9 3054.0 -16.335 < 2e-16 ***
MUP -72399.1 3552.9 -20.377 < 2e-16 ***
PROX_CBD -1111345.4 464953.2 -2.390 0.016887 *
PROX_HAWKER -198233.7 31913.3 -6.212 5.82e-10 ***
PROX_ELDERCARE -60342.5 34597.5 -1.744 0.081219 .
PROX_CHILDCARE -3350.9 2547.0 -1.316 0.188386
PROX_SUPERMARKET -50430.4 8520.0 -5.919 3.53e-09 ***
PROX_MALL -182216.1 36952.6 -4.931 8.53e-07 ***
PROX_TOPSCH -3131807.8 312900.9 -10.009 < 2e-16 ***
NUM_KINDERGARTEN -5929.8 1667.8 -3.556 0.000382 ***
NUM_CHILDCARE 2194.7 764.8 2.870 0.004134 **
NUM_SCH -766.1 1034.0 -0.741 0.458796
NUM_TOPSCH 10097.1 2658.6 3.798 0.000148 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 74130 on 3744 degrees of freedom
Multiple R-squared: 0.2737, Adjusted R-squared: 0.271
F-statistic: 100.8 on 14 and 3744 DF, p-value: < 2.2e-168.3 Preparing Publication Quality Table: gtsummary method
| Characteristic | Beta | 95% CI1 | p-value |
|---|---|---|---|
| (Intercept) | 217,932 | 186,745, 249,119 | <0.001 |
| floor_area_sqm | 4,379 | 3,955, 4,803 | <0.001 |
| HIP | -49,888 | -55,876, -43,900 | <0.001 |
| MUP | -72,399 | -79,365, -65,433 | <0.001 |
| PROX_CBD | -1,111,345 | -2,022,932, -199,759 | 0.017 |
| PROX_HAWKER | -198,234 | -260,803, -135,665 | <0.001 |
| PROX_ELDERCARE | -60,342 | -128,174, 7,489 | 0.081 |
| PROX_CHILDCARE | -3,351 | -8,345, 1,643 | 0.2 |
| PROX_SUPERMARKET | -50,430 | -67,135, -33,726 | <0.001 |
| PROX_MALL | -182,216 | -254,665, -109,767 | <0.001 |
| PROX_TOPSCH | -3,131,808 | -3,745,281, -2,518,335 | <0.001 |
| NUM_KINDERGARTEN | -5,930 | -9,200, -2,660 | <0.001 |
| NUM_CHILDCARE | 2,195 | 695, 3,694 | 0.004 |
| NUM_SCH | -766 | -2,793, 1,261 | 0.5 |
| NUM_TOPSCH | 10,097 | 4,885, 15,310 | <0.001 |
| 1 CI = Confidence Interval | |||
tbl_regression(resale_price.mlr,
intercept = TRUE) %>%
add_glance_source_note(
label = list(sigma ~ "\U03C3"),
include = c(r.squared, adj.r.squared,
AIC, statistic,
p.value, sigma))| Characteristic | Beta | 95% CI1 | p-value |
|---|---|---|---|
| (Intercept) | 217,932 | 186,745, 249,119 | <0.001 |
| floor_area_sqm | 4,379 | 3,955, 4,803 | <0.001 |
| HIP | -49,888 | -55,876, -43,900 | <0.001 |
| MUP | -72,399 | -79,365, -65,433 | <0.001 |
| PROX_CBD | -1,111,345 | -2,022,932, -199,759 | 0.017 |
| PROX_HAWKER | -198,234 | -260,803, -135,665 | <0.001 |
| PROX_ELDERCARE | -60,342 | -128,174, 7,489 | 0.081 |
| PROX_CHILDCARE | -3,351 | -8,345, 1,643 | 0.2 |
| PROX_SUPERMARKET | -50,430 | -67,135, -33,726 | <0.001 |
| PROX_MALL | -182,216 | -254,665, -109,767 | <0.001 |
| PROX_TOPSCH | -3,131,808 | -3,745,281, -2,518,335 | <0.001 |
| NUM_KINDERGARTEN | -5,930 | -9,200, -2,660 | <0.001 |
| NUM_CHILDCARE | 2,195 | 695, 3,694 | 0.004 |
| NUM_SCH | -766 | -2,793, 1,261 | 0.5 |
| NUM_TOPSCH | 10,097 | 4,885, 15,310 | <0.001 |
| R² = 0.274; Adjusted R² = 0.271; AIC = 94,988; Statistic = 101; p-value = <0.001; σ = 74,129 | |||
| 1 CI = Confidence Interval | |||
8.4 Checking for multicolinearity
Variables Tolerance VIF
1 floor_area_sqm 0.9431674 1.060257
2 HIP 0.8346136 1.198159
3 MUP 0.8375694 1.193931
4 PROX_CBD 0.4847280 2.063013
5 PROX_HAWKER 0.8941397 1.118393
6 PROX_ELDERCARE 0.9211740 1.085571
7 PROX_CHILDCARE 0.9362782 1.068059
8 PROX_SUPERMARKET 0.9730071 1.027742
9 PROX_MALL 0.5920959 1.688916
10 PROX_TOPSCH 0.4740046 2.109684
11 NUM_KINDERGARTEN 0.6802116 1.470131
12 NUM_CHILDCARE 0.6707035 1.490972
13 NUM_SCH 0.7610742 1.313932
14 NUM_TOPSCH 0.8840727 1.131129
8.5 Testing for Spatial Autocorrelation
resale_price.mlr.output <- as.data.frame(resale_price.mlr$residuals)
final_resale_price.mlr.sf <- cbind(train_resale_prices, resale_price.mlr.output) %>%
rename(`MLR_RES` = `resale_price.mlr.residuals`)
final_resale_price.mlr.sp <- as_Spatial(final_resale_price.mlr.sf)
final_resale_price.mlr.spclass : SpatialPointsDataFrame
features : 3759
extent : 11597.97, 45154.27, 28097.64, 48622.47 (xmin, xmax, ymin, ymax)
crs : +proj=tmerc +lat_0=1.36666666666667 +lon_0=103.833333333333 +k=1 +x_0=28001.642 +y_0=38744.572 +ellps=WGS84 +towgs84=0,0,0,0,0,0,0 +units=m +no_defs
variables : 24
names : floor_area_sqm, resale_price, HIP, MUP, PROX_CBD, PROX_HAWKER, PROX_ELDERCARE, PROX_KINDERGARTEN, PROX_CHILDCARE, PROX_BUSSTOP, PROX_MRT, PROX_SCH, PROX_SUPERMARKET, PROX_MALL, PROX_TOPSCH, ...
min values : 51, 240000, 0, 0, 0.001, 0.002, 0.001, 0.001, 0.002, 0.005, 0.001, 0.001, 0.001, 0.003, 0.001, ...
max values : 137, 958000, 1, 1, 0.013, 0.125, 0.133, 0.438, 1.912, 5.138, 0.217, 0.181, 0.526, 0.167, 0.02, ... 8.5.1 Map
tmap_mode("view")
tm_shape(subzone_sf)+
tmap_options(check.and.fix = TRUE) +
tm_polygons(alpha = 0.4) +
tm_shape(final_resale_price.mlr.sf) +
tm_dots(col = "MLR_RES",
alpha = 0.6,
style="quantile") +
tm_view(set.zoom.limits = c(11,14))
9 Building Hedonic Pricing Models using GWmodel
9.1 Building Fixed Bandwidth GWR Model
9.1.1 Computing fixed bandwith
set.seed(1234)
bw_adaptive <- bw.gwr(resale_price ~ floor_area_sqm + HIP + MUP + PROX_CBD + PROX_HAWKER + PROX_ELDERCARE + PROX_KINDERGARTEN + PROX_CHILDCARE + PROX_BUSSTOP + PROX_MRT + PROX_SUPERMARKET + PROX_MALL + PROX_TOPSCH + NUM_KINDERGARTEN + NUM_CHILDCARE + NUM_BUSSTOP + NUM_TOPSCH,
data=resale_price.sp,
approach="CV",
kernel="gaussian",
adaptive=TRUE,
longlat=FALSE)
As you can see from above, the most suitable bandwidth is 208.
set.seed(1234)
rf <- ranger(resale_price ~ floor_area_sqm + HIP + MUP + PROX_CBD + PROX_HAWKER + PROX_ELDERCARE + PROX_KINDERGARTEN + PROX_CHILDCARE + PROX_BUSSTOP + PROX_MRT + PROX_SUPERMARKET + PROX_MALL + PROX_TOPSCH + NUM_KINDERGARTEN + NUM_CHILDCARE + NUM_BUSSTOP + NUM_TOPSCH,
data=train_resale_prices)
print(rf)set.seed(1234)
grf_adaptive <- grf(formula = resale_price ~ floor_area_sqm + HIP + MUP + PROX_CBD + PROX_HAWKER + PROX_ELDERCARE + PROX_KINDERGARTEN + PROX_CHILDCARE + PROX_SUPERMARKET + PROX_MALL + PROX_TOPSCH + NUM_KINDERGARTEN + NUM_CHILDCARE + NUM_BUSSTOP + NUM_SCH + NUM_TOPSCH,
dframe=train_resale_prices,
bw=bw_adaptive,
kernel="adaptive",
coords=coords_train,
ntree = 30)